Add link to embedding description

Author: Conchylicultor

README.md

@@ -25,6 +25,8 @@ For now, DeepQA support the following dialog corpus:
  * [Ubuntu Dialogue Corpus](https://arxiv.org/abs/1506.08909) (thanks to [julien-c](https://github.com/julien-c)). Available using `--corpus ubuntu`. See the [instructions](data/ubuntu/) for installation.
  * Your own data (thanks to [julien-c](https://github.com/julien-c)) by using a simple custom conversation format (See [here](data/lightweight) for more info).
 
+To speedup the training, it's also possible to use pre-trained word embeddings (thanks to [Eschnou](https://github.com/eschnou)). More info [here](data/embeddings).
+
 ## Installation
 
 The program requires the following dependencies (easy to install using pip):

data/embeddings/README.md

@@ -1,7 +1,10 @@
-In order to use a pre-trained word2vec file, you must first download it and place it here. DeepQA supports both the .bin format of the Google News word2vec embeddings, and the .vec format of the Facebook fasttext embeddings. The vec2bin.py is a small utility script to convert a .vec to a .bin file, which reduces disk space and improve the loading time.
+In order to use a pre-trained word2vec file, you must first download it and place it here. DeepQA supports both the .bin format of the Google News word2vec embeddings, and the .vec format of the Facebook fasttext embeddings. The `vec2bin.py` is a small utility script to convert a .vec to a .bin file, which reduces disk space and improve the loading time.
 
 Usage:
+
+```
 python main.py --initEmbeddings --embeddingSource=wiki.en.bin
+```
 
 Google News embeddings:
 https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing